V0213 10:06:43.630000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "ea8d5f3b71d4c6f123369837b5701c7b"}
	{
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	"args": {
	"compile_id": "0/0"
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	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
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V0213 10:06:43.635000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/torch/_dynamo/convert_frame.py", 0]}
V0213 10:06:43.635000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/__run_lpar_main__.py", 1]}
V0213 10:06:43.636000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/__par__/meta_only/bootstrap.py", 2]}
V0213 10:06:43.636000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/__par__/bootstrap.py", 3]}
V0213 10:06:43.636000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py", 4]}
V0213 10:06:43.637000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/torch/export/__init__.py", 5]}
V0213 10:06:43.637000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/torch/export/_trace.py", 6]}
V0213 10:06:43.637000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/torch/export/exported_program.py", 7]}
V0213 10:06:43.638000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/torch/_dynamo/eval_frame.py", 8]}
V0213 10:06:43.638000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_logging/structured.py:23] {"str": ["/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/torch/nn/modules/module.py", 9]}
V0213 10:06:43.638000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/convert_frame.py:972] {"dynamo_start": {"stack": [{"line": 38, "name": "<module>", "filename": 1}, {"line": 35, "name": "__invoke_main", "filename": 1}, {"line": 98, "name": "run_as_main", "filename": 2}, {"line": 94, "name": "run_as_main", "filename": 3}, {"line": 46, "name": "main", "filename": 4}, {"line": 370, "name": "export", "filename": 5}, {"line": 1019, "name": "wrapper", "filename": 6}, {"line": 121, "name": "wrapper", "filename": 7}, {"line": 2080, "name": "_export", "filename": 6}, {"line": 1019, "name": "wrapper", "filename": 6}, {"line": 121, "name": "wrapper", "filename": 7}, {"line": 1945, "name": "_export_for_training", "filename": 6}, {"line": 1298, "name": "_strict_export_lower_to_aten_ir", "filename": 6}, {"line": 693, "name": "_export_to_torch_ir", "filename": 6}, {"line": 1587, "name": "inner", "filename": 8}, {"line": 1751, "name": "_wrapped_call_impl", "filename": 9}, {"line": 1762, "name": "_call_impl", "filename": 9}, {"line": 570, "name": "_fn", "filename": 8}, {"line": 1751, "name": "_wrapped_call_impl", "filename": 9}, {"line": 30, "name": "forward", "filename": 4}]}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.639000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "aae7090d6ef9e393cd4c1db18cc6aa12"}
	{
	"name": "entire_frame_compile",
	"ts": 1739470003639467.8,
	"args": {
	"fn_name": "_compile.compile_inner",
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
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V0213 10:06:43.649000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_subclasses/meta_utils.py:248] {"describe_storage": {"id": 0, "describer_id": 0, "size": 320}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.650000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_subclasses/meta_utils.py:462] {"describe_tensor": {"id": 0, "ndim": 2, "dtype": "torch.float32", "device": "device(type='cuda', index=0)", "size": [8, 10], "is_leaf": true, "stride": [10, 1], "storage": 0, "view_func": "_CustomViewFunc(func=<built-in method _view_func_unsafe of Tensor object at 0x7f88a78af9c0>)", "describer_id": 0}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.650000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_subclasses/meta_utils.py:1867] {"describe_source": {"describer_id": 0, "id": 0, "source": "L['x']"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.652000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_subclasses/meta_utils.py:248] {"describe_storage": {"id": 1, "describer_id": 0, "size": 320}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.653000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_subclasses/meta_utils.py:462] {"describe_tensor": {"id": 1, "ndim": 2, "dtype": "torch.float32", "device": "device(type='cuda', index=0)", "size": [8, 10], "is_leaf": true, "stride": [10, 1], "storage": 1, "view_func": "_CustomViewFunc(func=<built-in method _view_func_unsafe of Tensor object at 0x7f88a9d20b80>)", "describer_id": 0}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.653000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_subclasses/meta_utils.py:1867] {"describe_source": {"describer_id": 0, "id": 1, "source": "L['y']"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
V0213 10:06:43.676000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/output_graph.py:1361] {"dynamo_output_graph": {"sizes": {"l_x_": [8, 10], "x": [8, 16], "x_1": [8, 16], "x_2": [8, 16]}}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "4cc363070b9fbf0808a57330cebb1697"}
	class GraphModule(torch.nn.Module):
	    def forward(self, L_x_: "f32[8, 10][10, 1]cuda:0"):
	        l_x_ = L_x_
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:31 in forward, code: x = self.fc1(x)
	        x: "f32[8, 16][16, 1]cuda:0" = self.L__self___fc1(l_x_);  l_x_ = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:32 in forward, code: x = self.relu(x)
	        x_1: "f32[8, 16][16, 1]cuda:0" = self.L__self___relu(x);  x = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:33 in forward, code: x = self.sigmoid(x)
	        x_2: "f32[8, 16][16, 1]cuda:0" = self.L__self___sigmoid(x_1);  x_1 = None
	        return (x_2,)
	        
V0213 10:06:43.677000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "e3f07a427b7031ae9a163c1a4afddaa0"}
	{
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	"ts": 1739470003677287.8,
	"args": {
	"fn_name": "OutputGraph.call_user_compiler",
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	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
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V0213 10:06:43.678000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "71f754abe045555e3ec3b2dfe8601c7e"}
	{
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	"args": {
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	"ph": "E",
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V0213 10:06:43.693000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/guards.py:2502] {"dynamo_cpp_guards_str": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "1667e725bf83806b63b2c7537282ed5c"}
	
	TREE_GUARD_MANAGER:
	+- RootGuardManager
	| +- DEFAULT_DEVICE: utils_device.CURRENT_DEVICE == None                           # _dynamo/output_graph.py:491 in init_ambient_guards
	| +- GLOBAL_STATE: ___check_global_state()
	| +- TORCH_FUNCTION_MODE_STACK: ___check_torch_function_mode_stack()
	| +- GuardManager: source=L['x'], accessed_by=FrameLocalsGuardAccessor(key='x', framelocals_idx=1)
	| | +- TYPE_MATCH: ___check_type_id(L['x'], 140225372413968)                   
	| | +- NO_HASATTR: hasattr(L['x'], '_dynamo_dynamic_indices') == False         
	| +- GuardManager: source=L['y'], accessed_by=FrameLocalsGuardAccessor(key='y', framelocals_idx=2)
	| | +- TYPE_MATCH: ___check_type_id(L['y'], 140225372413968)                   
	| | +- NO_HASATTR: hasattr(L['y'], '_dynamo_dynamic_indices') == False         
	| +- GuardManager: source=L['self'], accessed_by=FrameLocalsGuardAccessor(key='self', framelocals_idx=0)
	| | +- ID_MATCH: ___check_obj_id(L['self'], 140226411458672)                 
	| | +- GuardManager: source=L['self'].__dict__, accessed_by=GetGenericDictGuardAccessor
	| | | +- GuardManager: source=L['self'].training, accessed_by=DictGetItemGuardAccessor('training')
	| | | | +- ID_MATCH: ___check_obj_id(L['self'].training, 140226838509560)        
	| | | +- GuardManager: source=L['self']._modules, accessed_by=DictGetItemGuardAccessor('_modules')
	| | | | +- GuardManager: source=L['self'].fc1, accessed_by=DictGetItemGuardAccessor('fc1')
	| | | | | +- ID_MATCH: ___check_obj_id(L['self'].fc1, 140226411461696)             
	| | | | | +- GuardManager: source=L['self'].fc1.__dict__, accessed_by=GetGenericDictGuardAccessor
	| | | | | | +- GuardManager: source=L['self'].fc1.training, accessed_by=DictGetItemGuardAccessor('training')
	| | | | | | | +- ID_MATCH: ___check_obj_id(L['self'].fc1.training, 140226838509560)    
	| | | | +- GuardManager: source=L['self'].relu, accessed_by=DictGetItemGuardAccessor('relu')
	| | | | | +- ID_MATCH: ___check_obj_id(L['self'].relu, 140226412360752)            
	| | | | | +- GuardManager: source=L['self'].relu.__dict__, accessed_by=GetGenericDictGuardAccessor
	| | | | | | +- GuardManager: source=L['self'].relu.training, accessed_by=DictGetItemGuardAccessor('training')
	| | | | | | | +- ID_MATCH: ___check_obj_id(L['self'].relu.training, 140226838509560)   
	| | | | +- GuardManager: source=L['self'].sigmoid, accessed_by=DictGetItemGuardAccessor('sigmoid')
	| | | | | +- ID_MATCH: ___check_obj_id(L['self'].sigmoid, 140226366522032)         
	| | | | | +- GuardManager: source=L['self'].sigmoid.__dict__, accessed_by=GetGenericDictGuardAccessor
	| | | | | | +- GuardManager: source=L['self'].sigmoid.training, accessed_by=DictGetItemGuardAccessor('training')
	| | | | | | | +- ID_MATCH: ___check_obj_id(L['self'].sigmoid.training, 140226838509560)
	+- LAMBDA_GUARD: L['x'].size()[0] == 8  # (unknown source L['x'].size()[0], please file a bug)
	+- LAMBDA_GUARD: L['x'].size()[1] == 10  # (unknown source L['x'].size()[1], please file a bug)
	+- LAMBDA_GUARD: L['x'].stride()[0] == 10  # (unknown source L['x'].stride()[0], please file a bug)
	+- LAMBDA_GUARD: L['x'].stride()[1] == 1  # (unknown source L['x'].stride()[1], please file a bug)
	+- LAMBDA_GUARD: L['x'].storage_offset() == 0  # (unknown source L['x'].storage_offset(), please file a bug)
	+- LAMBDA_GUARD: L['y'].size()[0] == 8  # (unknown source L['y'].size()[0], please file a bug)
	+- LAMBDA_GUARD: L['y'].size()[1] == 10  # (unknown source L['y'].size()[1], please file a bug)
	+- LAMBDA_GUARD: L['y'].stride()[0] == 10  # (unknown source L['y'].stride()[0], please file a bug)
	+- LAMBDA_GUARD: L['y'].stride()[1] == 1  # (unknown source L['y'].stride()[1], please file a bug)
	+- LAMBDA_GUARD: L['y'].storage_offset() == 0  # (unknown source L['y'].storage_offset(), please file a bug)
	
	Guard latency = 0.00 us
V0213 10:06:43.694000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "aea22c72a899c439a2fba9e9445b187a"}
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	"cat": "dynamo_timed",
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V0213 10:06:43.695000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "b3df764a799b88525ad909435111cf20"}
	{
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	"ph": "B",
	"cat": "dynamo_timed",
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V0213 10:06:43.696000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "c231f7bfdfd60d6a49db5112835aacfd"}
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V0213 10:06:43.717000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1463] {"compilation_metrics": {"compile_id": "0/0", "frame_key": "1", "co_name": "forward", "co_filename": "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py", "co_firstlineno": 30, "cache_size": 0, "accumulated_cache_size": 0, "guard_count": 11, "shape_env_guard_count": 0, "graph_op_count": 3, "graph_node_count": 5, "graph_input_count": 1, "start_time": 1739470003.634051, "entire_frame_compile_time_s": 0.054591, "backend_compile_time_s": 0.001389, "inductor_compile_time_s": null, "code_gen_time_s": null, "fail_type": null, "fail_reason": null, "fail_user_frame_filename": null, "fail_user_frame_lineno": null, "non_compliant_ops": [], "compliant_custom_ops": [], "restart_reasons": [], "dynamo_time_before_restart_s": 0.0, "has_guarded_code": true, "remote_cache_time_saved_s": null, 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V0213 10:06:43.722000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "f8df32b7fa743bacc15d3b2e5ebc59c2"}
	{
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V0213 10:06:45.422000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_inductor/compile_fx.py:1917] {"inductor_pre_grad_graph": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "c11229293a126f817cae1199c1d728e9"}
	class GraphModule(torch.nn.Module):
	    def forward(self, x: "f32[8, 10][10, 1]cuda:0", y: "f32[8, 10][10, 1]cuda:0"):
	        # No stacktrace found for following nodes
	        fc1_weight: "f32[16, 10][10, 1]cuda:0" = self.fc1.weight
	        fc1_bias: "f32[16][1]cuda:0" = self.fc1.bias
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:31 in forward, code: x = self.fc1(x)
	        linear: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.linear.default(x, fc1_weight, fc1_bias);  x = fc1_weight = fc1_bias = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:32 in forward, code: x = self.relu(x)
	        relu: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.relu.default(linear);  linear = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:33 in forward, code: x = self.sigmoid(x)
	        sigmoid: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.sigmoid.default(relu);  relu = None
	        return (sigmoid,)
	        
	
	 # graph id: 140226366523760
V0213 10:06:45.425000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "ef4309046cba5a4a110e5faae35341f2"}
	{
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	"ph": "B",
	"cat": "dynamo_timed",
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V0213 10:06:46.446000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "8626f5835186e5ea53bd516c7912a591"}
	{
	"name": "_recursive_pre_grad_passes",
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	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
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V0213 10:06:46.450000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "b7600b1631cfebbcd8d8b776ea39a533"}
	{
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	"ph": "B",
	"cat": "dynamo_timed",
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V0213 10:06:46.490000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py:214] {"artifact": {"name": "aot_forward_graph_fw_metadata", "encoding": "string"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "08abad34ef623f7fda57b39a4ce21e13"}
	ViewAndMutationMeta(input_info=[InputAliasInfo(is_leaf=True,
	                                              mutates_data=False,
	                                              mutates_metadata=False,
	                                              mutations_hidden_from_autograd=True,
	                                              mutations_under_no_grad_or_inference_mode=False,
	                                              mutation_inductor_storage_resize=False,
	                                              mutates_storage_metadata=False,
	                                              requires_grad=True,
	                                              keep_input_mutations=False),
	                               InputAliasInfo(is_leaf=True,
	                                              mutates_data=False,
	                                              mutates_metadata=False,
	                                              mutations_hidden_from_autograd=True,
	                                              mutations_under_no_grad_or_inference_mode=False,
	                                              mutation_inductor_storage_resize=False,
	                                              mutates_storage_metadata=False,
	                                              requires_grad=True,
	                                              keep_input_mutations=False),
	                               InputAliasInfo(is_leaf=True,
	                                              mutates_data=False,
	                                              mutates_metadata=False,
	                                              mutations_hidden_from_autograd=True,
	                                              mutations_under_no_grad_or_inference_mode=False,
	                                              mutation_inductor_storage_resize=False,
	                                              mutates_storage_metadata=False,
	                                              requires_grad=False,
	                                              keep_input_mutations=False),
	                               InputAliasInfo(is_leaf=True,
	                                              mutates_data=False,
	                                              mutates_metadata=False,
	                                              mutations_hidden_from_autograd=True,
	                                              mutations_under_no_grad_or_inference_mode=False,
	                                              mutation_inductor_storage_resize=False,
	                                              mutates_storage_metadata=False,
	                                              requires_grad=False,
	                                              keep_input_mutations=False)],
	                    output_info=[OutputAliasInfo(output_type=<OutputType.non_alias: 1>,
	                                                raw_type=<class 'torch._subclasses.functional_tensor.FunctionalTensor'>,
	                                                base_idx=None,
	                                                dynamic_dims=set(),
	                                                requires_grad=False,
	                                                functional_tensor=None)],
	                    num_intermediate_bases=0,
	                    keep_input_mutations=False,
	                    traced_tangents=[],
	                    subclass_inp_meta=[PlainTensorMeta(unwrapped_idx=0,
	                                                      memory_format=None),
	                                      PlainTensorMeta(unwrapped_idx=1,
	                                                      memory_format=None),
	                                      PlainTensorMeta(unwrapped_idx=2,
	                                                      memory_format=None),
	                                      PlainTensorMeta(unwrapped_idx=3,
	                                                      memory_format=None)],
	                    subclass_fw_graph_out_meta=[PlainTensorMeta(unwrapped_idx=0,
	                                                               memory_format=None)],
	                    subclass_tangent_meta=[],
	                    is_train=False,
	                    traced_tangent_metas=None,
	                    num_symints_saved_for_bw=None,
	                    grad_enabled_mutation=None,
	                    deterministic=None,
	                    static_input_indices=[],
	                    tokens={},
	                    indices_of_inputs_that_requires_grad_with_mutations_in_bw=[],
	                    bw_donated_idxs=None,
	                    num_backward_tokens=0)
V0213 10:06:46.492000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py:232] {"aot_inference_graph": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "f0533770d65bc6c53bcea908c21cdc24"}
	class <lambda>(torch.nn.Module):
	    def forward(self, arg0_1: "f32[16, 10][10, 1]cuda:0", arg1_1: "f32[16][1]cuda:0", arg2_1: "f32[8, 10][10, 1]cuda:0", arg3_1: "f32[8, 10][10, 1]cuda:0"):
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:31 in forward, code: x = self.fc1(x)
	        permute: "f32[10, 16][1, 10]cuda:0" = torch.ops.aten.permute.default(arg0_1, [1, 0]);  arg0_1 = None
	        addmm: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.addmm.default(arg1_1, arg2_1, permute);  arg1_1 = arg2_1 = permute = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:32 in forward, code: x = self.relu(x)
	        relu: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.relu.default(addmm);  addmm = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:33 in forward, code: x = self.sigmoid(x)
	        sigmoid: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.sigmoid.default(relu);  relu = None
	        return (sigmoid,)
	        
V0213 10:06:46.496000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "b9c2f5429d306e08ee53c8c03691edc5"}
	{
	"name": "create_aot_dispatcher_function",
	"ts": 1739470006496217.2,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:46.498000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "10a2caea4dabc26d6518ce65704c761d"}
	{
	"name": "compile_fx.<locals>.fw_compiler_base",
	"ts": 1739470006498870.0,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:46.499000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "ebaf33c7d9a820229016b3c0e5d75781"}
	{
	"name": "_recursive_joint_graph_passes",
	"ts": 1739470006499729.0,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:46.690000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "7db5597e4dd0e10621257d440e6fc8df"}
	{
	"name": "pad_mm_benchmark",
	"ts": 1739470006690189.5,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:46.692000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "ddaead9f424e392571f154185b14cfaf"}
	{
	"name": "pad_mm_benchmark_get_do_bench",
	"ts": 1739470006692014.2,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:46.693000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "8156f5997cad2bdf911b631d7e2563be"}
	{
	"name": "pad_mm_benchmark_get_do_bench",
	"ts": 1739470006693572.2,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.476000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "a6b01932b479b020a3a466e8116a4aa6"}
	{
	"name": "TritonBenchmarker.benchmark_gpu",
	"ts": 1739470007476532.2,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.654000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "5ff3df0385f9c7f5aa3538782bd0b2a8"}
	{
	"name": "TritonBenchmarker.benchmark_gpu",
	"ts": 1739470007654361.0,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.657000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "b9040f5ceeb3ee1048747ac62e585ba7"}
	{
	"name": "TritonBenchmarker.benchmark_gpu",
	"ts": 1739470007657114.0,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.801000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "8cd0a2b800882a29fee9f49e3fb33154"}
	{
	"name": "TritonBenchmarker.benchmark_gpu",
	"ts": 1739470007801626.5,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.805000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "e7eb86c4355474816509513f4fe06ab4"}
	{
	"name": "pad_mm_benchmark",
	"ts": 1739470007804908.8,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.807000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "ac80ca82560849141fa2bfd46586e702"}
	{
	"name": "_recursive_joint_graph_passes",
	"ts": 1739470007806972.5,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.808000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "7426573358f065667fc889240ff9e7aa"}
	{
	"name": "inductor_compile",
	"ts": 1739470007808676.2,
	"args": {
	"fn_name": "compile_fx_inner",
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:47.820000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_inductor/compile_fx.py:923] {"artifact": {"name": "fx_graph_runnable", "encoding": "string"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "5d01e2e5d7f49d7bbed81cd272bc2dd2"}
	
	import os
	os.environ['TORCH_TRACE'] = '/home/shangdiy/my_trace_log_dir'
	os.environ['TORCH_COMPILE_DEBUG'] = '1'
	os.environ['TORCH_LOGS'] = '+inductor'
	os.environ['PYTORCH_DDP_USE_SIDE_STREAM'] = '0'
	os.environ['TRITON_CACHE_MANAGER'] = 'triton.runtime.cache:RemoteCacheManager'
	os.environ['TRITON_REMOTE_CACHE_BACKEND'] = 'triton.fb.fb_memcache:FbMemcacheRemoteKernelCache'
	os.environ['TORCHINDUCTOR_CACHE_DIR'] = '/tmp/torchinductor_shangdiy'
	
	import torch
	from torch import tensor, device
	import torch.fx as fx
	from torch._dynamo.testing import rand_strided
	from math import inf
	import torch._inductor.inductor_prims
	
	import torch._dynamo.config
	import torch._inductor.config
	import torch._functorch.config
	import torch.fx.experimental._config
	torch._dynamo.config.specialize_int = False
	torch._dynamo.config.specialize_float = False
	torch._dynamo.config.assume_static_by_default = True
	torch._dynamo.config.automatic_dynamic_shapes = True
	torch._dynamo.config.capture_scalar_outputs = False
	torch._dynamo.config.capture_dynamic_output_shape_ops = False
	torch._dynamo.config.prefer_deferred_runtime_asserts_over_guards = False
	torch._dynamo.config.allow_complex_guards_as_runtime_asserts = False
	torch._dynamo.config.allow_rnn = False
	torch._inductor.config.cpp_wrapper = True
	torch._inductor.config.triton.cudagraphs = False
	torch._inductor.config.triton.autotune_cublasLt = False
	torch._inductor.config.triton.autotune_at_compile_time = True
	torch._inductor.config.triton.store_cubin = True
	torch._inductor.config.aot_inductor.output_path = 'clfxsfxv4tjfonuwp2w6qbwpgbdh7xotkmm5r6jfm6i6nvldgzlp'
	torch._inductor.config.aot_inductor.serialized_in_spec = '[1, {"type": "builtins.tuple", "context": "null", "children_spec": [{"type": "builtins.tuple", "context": "null", "children_spec": [{"type": null, "context": null, "children_spec": []}, {"type": null, "context": null, "children_spec": []}]}, {"type": "builtins.dict", "context": "[]", "children_spec": []}]}]'
	torch._inductor.config.aot_inductor.serialized_out_spec = '[1, {"type": null, "context": null, "children_spec": []}]'
	torch._inductor.config.aot_inductor.package = True
	torch._functorch.config.functionalize_rng_ops = False
	torch._functorch.config.fake_tensor_allow_unsafe_data_ptr_access = True
	torch._functorch.config.unlift_effect_tokens = False
	
	
	
	isolate_fails_code_str = None
	
	torch.ops.load_library("//caffe2/torch/fb/sparsenn:sparsenn_operators_gpu")
	torch.ops.load_library("//caffe2/torch/fb/sparsenn:sparsenn_operators")
	torch.ops.load_library("//deeplearning/fbgemm/fbgemm_gpu:sparse_ops_cpu")
	torch.ops.load_library("//deeplearning/fbgemm/fbgemm_gpu:sparse_ops")
	
	"""
	To run this script in fbcode:
	- Create a directory (//scripts/{your_unixname}/repro)
	- Put this file in scripts/{your_unixname}/repro/fx_graph_runnable.py
	- Add a TARGETS file that looks like the following
	- `buck2 run //scripts/{your_unixname}/repro:repro`
	
	NOTE: you may need additional deps to actually be able to run the script.
	```
	# Contents of TARGETS file
	load("@fbcode_macros//build_defs:python_binary.bzl", "python_binary")
	
	python_binary(
	    name = "repro",
	    main_src = "fx_graph_runnable.py",
	    deps = [
	        "//caffe2:torch",
	        "//caffe2/torch/fb/sparsenn:sparsenn_operators_gpu",
	        "//caffe2/torch/fb/sparsenn:sparsenn_operators",
	        "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops_cpu",
	        "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops",
	    ],
	)
	```
	"""
	
	# torch version: 2.7.0a0+fb
	# torch cuda version: 12.4.0
	# CUDA Info: 
	# nvcc: NVIDIA (R) Cuda compiler driver 
	# Copyright (c) 2005-2023 NVIDIA Corporation 
	# Built on Fri_Jan__6_16:45:21_PST_2023 
	# Cuda compilation tools, release 12.0, V12.0.140 
	# Build cuda_12.0.r12.0/compiler.32267302_0 
	
	# GPU Hardware Info: 
	# NVIDIA PG509-210 : 8 
	
	
	from torch.nn import *
	class Repro(torch.nn.Module):
	    def __init__(self) -> None:
	        super().__init__()
	        self.fc1 = Module().cuda()
	
	    
	    
	    def forward(self):
	        arg2_1, arg3_1, = fx_pytree.tree_flatten_spec([], self._in_spec)
	        fc1_weight = self.fc1.weight
	        fc1_bias = self.fc1.bias
	        permute = torch.ops.aten.permute.default(fc1_weight, [1, 0]);  fc1_weight = None
	        addmm = torch.ops.aten.addmm.default(fc1_bias, arg2_1, permute);  fc1_bias = arg2_1 = permute = None
	        relu = torch.ops.aten.relu.default(addmm);  addmm = None
	        sigmoid = torch.ops.aten.sigmoid.default(relu);  relu = None
	        return (sigmoid,)
	        
	def load_args(reader):
	    buf0 = reader.storage(None, 320, device=device(type='cuda', index=0))
	    reader.tensor(buf0, (8, 10), is_leaf=True)  # arg2_1
	    buf1 = reader.storage(None, 320, device=device(type='cuda', index=0))
	    reader.tensor(buf1, (8, 10), is_leaf=True)  # arg3_1
	load_args._version = 0
	mod = Repro()
	if __name__ == '__main__':
	    from torch._dynamo.repro.after_aot import run_repro
	    with torch.no_grad():
	        run_repro(mod, load_args, accuracy=False, command='run', save_dir=None, tracing_mode='real', check_str=None)
	        # To run it separately, do 
	        # mod, args = run_repro(mod, load_args, accuracy=False, command='get_args', save_dir=None, tracing_mode='real', check_str=None)
	        # mod(*args)
V0213 10:06:47.831000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "1154151d9a689da1d9e1a4e8451e1d14"}
	{
	"name": "_recursive_post_grad_passes",
	"ts": 1739470007831592.0,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "B",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:48.304000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "3b3e2da7397d9d0018f1bc37ca3328ac"}
	{
	"name": "_recursive_post_grad_passes",
	"ts": 1739470008304419.2,
	"args": {
	"compile_id": "0/0"
	},
	"ph": "E",
	"cat": "dynamo_timed",
	"tid": 0,
	"pid": 0
	}
V0213 10:06:48.309000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_inductor/compile_fx.py:986] {"inductor_post_grad_graph": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "924b310c3f181a771d10c9e540fd375c"}
	class <lambda>(torch.nn.Module):
	    def forward(self):
	        arg2_1: "f32[8, 10][10, 1]cuda:0"; arg3_1: "f32[8, 10][10, 1]cuda:0"; 
	    
	        arg2_1, arg3_1, = fx_pytree.tree_flatten_spec([], self._in_spec)
	        # No stacktrace found for following nodes
	        fc1_weight: "f32[16, 10][10, 1]cuda:0" = self.fc1.weight
	        fc1_bias: "f32[16][1]cuda:0" = self.fc1.bias
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:31 in forward, code: x = self.fc1(x)
	        permute: "f32[10, 16][1, 10]cuda:0" = torch.ops.aten.permute.default(fc1_weight, [1, 0]);  fc1_weight = None
	        
	        # No stacktrace found for following nodes
	        mm_default: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.mm.default(arg2_1, permute);  arg2_1 = permute = None
	        add_tensor: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.add.Tensor(mm_default, fc1_bias);  mm_default = fc1_bias = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:32 in forward, code: x = self.relu(x)
	        relu: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.relu.default(add_tensor);  add_tensor = None
	        
	         # File: /data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/7adc9a57403be96c/scripts/shangdiy/__aot__/aot#link-tree/scripts/shangdiy/aot.py:33 in forward, code: x = self.sigmoid(x)
	        sigmoid: "f32[8, 16][16, 1]cuda:0" = torch.ops.aten.sigmoid.default(relu);  relu = None
	        return (sigmoid,)
	        
V0213 10:06:48.310000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_inductor/compile_fx.py:996] {"artifact": {"name": "inductor_post_to_pre_grad_nodes", "encoding": "json"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "0bd866df1128fc8c7c07e4714969e2fb"}
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V0213 10:06:48.856000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "67a0f122bbb9fca97d997d01255a0a1e"}
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	#include <torch/csrc/inductor/aoti_include/cuda.h>
	// Definition of AOTI runtime interface functions
	
	#include <torch/csrc/inductor/aoti_runtime/interface.h>
	#include <torch/csrc/inductor/aoti_runtime/model_container.h>
	
	#include <iostream>
	#include <sstream>
	#include <stdexcept>
	#include <vector>
	
	#define CONVERT_EXCEPTION_TO_ERROR_CODE(...)                 \
	  try {                                                      \
	    __VA_ARGS__                                              \
	  } catch (const std::exception& e) {                        \
	    std::cerr << "Error: " << e.what() << std::endl;         \
	    return AOTI_RUNTIME_FAILURE;                             \
	  } catch (...) {                                            \
	    std::cerr << "Unknown exception occurred." << std::endl; \
	    return AOTI_RUNTIME_FAILURE;                             \
	  }                                                          \
	  return AOTI_RUNTIME_SUCCESS;
	
	#define AOTI_VECTOR_SIZE_CHECK(actual_size, expected_size, name)  \
	  do {                                                            \
	    AOTI_RUNTIME_CHECK(                                           \
	        actual_size == expected_size,                             \
	        "expected " + std::string(name) + " vector size to be " + \
	            std::to_string(expected_size) + ", but got " +        \
	            std::to_string(actual_size));                         \
	  } while (0)
	
	// AOTInductor uses at::addmm_out, which doesn't supports
	// arguments that requires gradient. For this reason, we
	// enforce no_grad context for run APIs.
	//
	// A RAII, thread local (!) guard that enables or disables grad mode upon
	// construction, and sets it back to the original value upon destruction.
	struct AOTINoGradGuard {
	  AOTINoGradGuard() : prev_mode(aoti_torch_grad_mode_is_enabled()) {
	    aoti_torch_grad_mode_set_enabled(false);
	  }
	  ~AOTINoGradGuard() {
	    aoti_torch_grad_mode_set_enabled(prev_mode);
	  }
	  bool prev_mode;
	};
	
	extern "C" {
	
	AOTIRuntimeError AOTInductorModelContainerCreate(
	    AOTInductorModelContainerHandle* container_handle,
	    size_t num_models,
	    bool is_cpu,
	    const char* cubin_dir) {
	      return AOTInductorModelContainerCreateWithDevice(
	        container_handle,
	        num_models,
	        is_cpu ? "cpu" : "cuda",
	        cubin_dir);
	}
	
	AOTIRuntimeError AOTInductorModelContainerCreateWithDevice(
	    AOTInductorModelContainerHandle* container_handle,
	    size_t num_models,
	    const char* device_str,
	    const char* cubin_dir) {
	  if (num_models == 0) {
	    std::cerr << "Error: num_models must be positive, but got 0" << std::endl;
	    return AOTI_RUNTIME_FAILURE;
	  }
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    std::optional<std::string> cubin_dir_opt;
	    if (cubin_dir != nullptr) {
	      cubin_dir_opt.emplace(cubin_dir);
	    }
	    auto* container = new torch::aot_inductor::AOTInductorModelContainer(
	        num_models, std::string(device_str), cubin_dir_opt);
	    *container_handle =
	        reinterpret_cast<AOTInductorModelContainerHandle>(container);
	  })
	}
	
	AOTIRuntimeError AOTInductorModelContainerDelete(
	    AOTInductorModelContainerHandle container_handle) {
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    auto* container =
	        reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	            container_handle);
	    delete container;
	  });
	}
	
	AOTIRuntimeError AOTInductorModelContainerRun(
	    AOTInductorModelContainerHandle container_handle,
	    AtenTensorHandle* input_handles, // array of input AtenTensorHandle; handles
	                                     // are stolen; the array itself is borrowed
	    size_t num_inputs,
	    AtenTensorHandle*
	        output_handles, // array for writing output AtenTensorHandle; handles
	                        // will be stolen by the caller; the array itself is
	                        // borrowed
	    size_t num_outputs,
	    AOTInductorStreamHandle stream_handle,
	    AOTIProxyExecutorHandle proxy_executor_handle) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  AOTI_VECTOR_SIZE_CHECK(num_inputs, container->num_inputs(), "inputs");
	  AOTI_VECTOR_SIZE_CHECK(num_outputs, container->num_outputs(), "outputs");
	
	  auto stream =
	      reinterpret_cast<torch::aot_inductor::DeviceStreamType>(stream_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    AOTINoGradGuard guard;
	    container->run(
	        input_handles, output_handles, stream, proxy_executor_handle);
	  })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetNumConstants(
	    AOTInductorModelContainerHandle container_handle,
	    size_t* num_constants) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	    { *num_constants = container->num_constants(); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetConstantName(
	    AOTInductorModelContainerHandle container_handle,
	    size_t idx,
	    const char** name) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	    { *name = container->constant_name(idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetConstantOriginalFQN(
	    AOTInductorModelContainerHandle container_handle,
	    size_t idx,
	    const char** original_fqn) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	    { *original_fqn = container->constant_original_fqn(idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetConstantFromFolded(
	    AOTInductorModelContainerHandle container_handle,
	    size_t idx,
	    bool* from_folded) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({ *from_folded = container->constant_from_folded(idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetConstantType(
	    AOTInductorModelContainerHandle container_handle,
	    size_t idx,
	    int32_t* type) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({ *type = container->constant_type(idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetConstantDtype(
	    AOTInductorModelContainerHandle container_handle,
	    size_t idx,
	    int32_t* dtype) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	    { *dtype = container->constant_dtype(idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerUpdateConstantBuffer(
	    AOTInductorModelContainerHandle container_handle,
	    AOTInductorConstantMapHandle constant_map_handle,
	    bool use_inactive,
	    bool validate_full_update) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  auto input_map = reinterpret_cast<std::unordered_map<std::string, AtenTensorHandle>*>(constant_map_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    container->update_constant_buffer(
	        *input_map, use_inactive, validate_full_update);
	  })
	}
	
	AOTIRuntimeError AOTInductorModelContainerUpdateInactiveConstantBuffer(
	    AOTInductorModelContainerHandle container_handle,
	    AOTInductorConstantMapHandle constant_map_handle) {
	  return AOTInductorModelContainerUpdateConstantBuffer(container_handle,
	          constant_map_handle,
	          /*use_inactive*/ true,
	          /*validate_full_update*/ true);
	}
	
	AOTIRuntimeError AOTInductorModelContainerRunConstantFolding(
	    AOTInductorModelContainerHandle container_handle,
	    bool use_inactive,
	    AOTInductorStreamHandle stream_handle,
	    AOTIProxyExecutorHandle proxy_executor_handle) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  auto stream =
	      reinterpret_cast<torch::aot_inductor::DeviceStreamType>(stream_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    AOTINoGradGuard guard;
	    container->run_const_fold(use_inactive, stream, proxy_executor_handle);
	  })
	}
	
	AOTIRuntimeError AOTInductorModelContainerSwapConstantBuffer(
	    AOTInductorModelContainerHandle container_handle) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    container->swap_constant_buffer();
	  })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetNumInputs(
	    AOTInductorModelContainerHandle container_handle,
	    size_t* ret_num_inputs) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	      { *ret_num_inputs = container->num_inputs(); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetInputName(
	    AOTInductorModelContainerHandle container_handle,
	    size_t input_idx,
	    const char** ret_input_names) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	      { *ret_input_names = container->input_name(input_idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetNumOutputs(
	    AOTInductorModelContainerHandle container_handle,
	    size_t* ret_num_outputs) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	      { *ret_num_outputs = container->num_outputs(); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetOutputName(
	    AOTInductorModelContainerHandle container_handle,
	    size_t output_idx,
	    const char** ret_output_names) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE(
	      { *ret_output_names = container->output_name(output_idx); })
	}
	
	AOTIRuntimeError AOTInductorModelContainerGetCallSpec(
	    AOTInductorModelContainerHandle container_handle,
	    const char** in_spec,
	    const char** out_spec) {
	  auto* container =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModelContainer*>(
	          container_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    *in_spec = container->get_in_spec();
	    *out_spec = container->get_out_spec();
	  })
	}
	
	AOTIRuntimeError AOTInductorModelCreate(
	    AOTInductorModelHandle* model_handle,
	    AOTInductorConstantMapHandle constant_map_handle){
	    CONVERT_EXCEPTION_TO_ERROR_CODE({
	      auto constant_map = std::make_shared<torch::aot_inductor::ConstantMap>();
	      auto constant_array = std::make_shared<std::vector<torch::aot_inductor::ConstantHandle>>();
	      auto input_map = reinterpret_cast<std::unordered_map<std::string, AtenTensorHandle>*>(constant_map_handle);
	
	      auto model = new torch::aot_inductor::AOTInductorModel(
	          constant_map,
	          constant_array,
	          "cpu", // device_str is hardcoded, as AOTInductorModelCreate is only use for CPU models
	          ""
	      );
	
	      if (input_map) {
	        for (auto const& kv : *input_map) {
	          constant_map->emplace(kv.first, kv.second);
	        }
	      } else {
	        model->load_constants();
	      }
	
	      *model_handle = reinterpret_cast<AOTInductorModelHandle>(model);
	    })}
	
	AOTIRuntimeError AOTInductorModelRun(
	    AOTInductorModelHandle model_handle,
	    AtenTensorHandle* input_handles,
	    AtenTensorHandle* output_handles) {
	  auto model =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModel*>(model_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    AOTINoGradGuard guard;
	    model->run_impl(
	        input_handles,
	        output_handles,
	        (torch::aot_inductor::DeviceStreamType) nullptr,
	        nullptr);
	  })
	}
	
	AOTIRuntimeError AOTInductorModelDelete(AOTInductorModelHandle model_handle){
	    CONVERT_EXCEPTION_TO_ERROR_CODE({
	      auto model = reinterpret_cast<torch::aot_inductor::AOTInductorModel*>(
	          model_handle);
	      delete model;
	    })}
	
	AOTIRuntimeError AOTInductorModelGetNumOutputs(
	    AOTInductorModelHandle model_handle,
	    size_t* ret_num_outputs) {
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	      auto model = reinterpret_cast<torch::aot_inductor::AOTInductorModel*>(model_handle);
	      *ret_num_outputs = model->num_outputs();
	  })
	}
	
	AOTIRuntimeError AOTInductorModelUpdateConstantsMap(
	    AOTInductorModelHandle model_handle,
	    AOTInductorConstantMapHandle constant_map_handle) {
	  auto model =
	      reinterpret_cast<torch::aot_inductor::AOTInductorModel*>(model_handle);
	  CONVERT_EXCEPTION_TO_ERROR_CODE({
	    auto constant_map = std::make_shared<torch::aot_inductor::ConstantMap>();
	    auto input_map =
	        reinterpret_cast<std::unordered_map<std::string, AtenTensorHandle>*>(
	            constant_map_handle);
	
	    for (auto const& kv : *input_map) {
	      constant_map->emplace(kv.first, kv.second);
	    }
	    model->update_constants_map(std::move(constant_map));
	  })
	}
	
	} // extern "C"
	
	#define CUDA_DRIVER_CHECK(EXPR)                    \
	do {                                               \
	    CUresult code = EXPR;                          \
	    const char *msg;                               \
	    CUresult code_get_error = cuGetErrorString(code, &msg); \
	    if (code_get_error != CUDA_SUCCESS) {          \
	        throw std::runtime_error(                  \
	            std::string("CUDA driver error: ") +   \
	            std::string("invalid error code!"));   \
	    }                                              \
	    if (code != CUDA_SUCCESS) {                    \
	        throw std::runtime_error(                  \
	            std::string("CUDA driver error: ") +   \
	            std::string(msg));                     \
	    }                                              \
	} while (0);
	
	namespace {
	
	struct Grid {
	    Grid(uint32_t x, uint32_t y, uint32_t z)
	      : grid_x(x), grid_y(y), grid_z(z) {}
	    uint32_t grid_x;
	    uint32_t grid_y;
	    uint32_t grid_z;
	
	    bool is_non_zero() {
	        return grid_x > 0 && grid_y > 0 && grid_z > 0;
	    }
	};
	
	}  // anonymous namespace
	
	static inline CUfunction loadKernel(
	        std::string filePath,
	        const std::string &funcName,
	        uint32_t sharedMemBytes,
	        const std::optional<std::string> &cubinDir = std::nullopt) {
	    if (cubinDir) {
	        std::filesystem::path p1{*cubinDir};
	        std::filesystem::path p2{filePath};
	        filePath = (p1 / p2.filename()).string();
	    }
	
	    CUmodule mod;
	    CUfunction func;
	    CUDA_DRIVER_CHECK(cuModuleLoad(&mod, filePath.c_str()));
	    CUDA_DRIVER_CHECK(cuModuleGetFunction(&func, mod, funcName.c_str()));
	    if (sharedMemBytes > 0) {
	        CUDA_DRIVER_CHECK(cuFuncSetAttribute(
	            func,
	            CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES,
	            sharedMemBytes
	        ))
	    }
	    return func;
	}
	
	static inline void launchKernel(
	        CUfunction func,
	        uint32_t gridX,
	        uint32_t gridY,
	        uint32_t gridZ,
	        uint32_t numWarps,
	        uint32_t sharedMemBytes,
	        void* args[],
	        cudaStream_t stream) {
	    CUDA_DRIVER_CHECK(cuLaunchKernel(
	        func, gridX, gridY, gridZ, 32*numWarps, 1, 1, sharedMemBytes, stream, args, nullptr
	    ));
	}
	CACHE_TORCH_DTYPE(float32);
	CACHE_TORCH_DEVICE(cuda);
	CACHE_TORCH_LAYOUT(strided);
	namespace torch::aot_inductor {
	
	namespace {
	class AOTInductorModelKernels : public AOTInductorModelKernelsBase {
	  public:
	    CUfunction triton_poi_fused_addmm_relu_sigmoid_0{nullptr};
	};
	}  // namespace
	
	AOTInductorModel::AOTInductorModel(std::shared_ptr<ConstantMap> constants_map,
	                                   std::shared_ptr<std::vector<ConstantHandle>> constants_array,
	                                   const std::string& device_str,
	                                   std::optional<std::string> cubin_dir,
	                                   bool include_weights)
	    : AOTInductorModelBase(2, 1, 2, device_str, cubin_dir, true) {
	    inputs_info_[0].name = "arg2_1";
	    inputs_info_[1].name = "arg3_1";
	    constants_info_[0].name = "fc1_weight";
	    constants_info_[0].dtype = static_cast<int32_t>(cached_torch_dtype_float32);
	    constants_info_[0].offset = 0;
	    constants_info_[0].data_size = 640;
	    constants_info_[0].from_folded = false;
	    constants_info_[0].type = static_cast<int32_t>(torch::aot_inductor::ConstantType::Parameter);
	    constants_info_[0].shape = {16, 10};
	    constants_info_[0].stride = {10, 1};
	    constants_info_[0].layout = static_cast<int32_t>(cached_torch_layout_strided);
	    constants_info_[0].original_fqn = "fc1.weight";
	    constants_info_[1].name = "fc1_bias";
	    constants_info_[1].dtype = static_cast<int32_t>(cached_torch_dtype_float32);
	    constants_info_[1].offset = 0;
	    constants_info_[1].data_size = 64;
	    constants_info_[1].from_folded = false;
	    constants_info_[1].type = static_cast<int32_t>(torch::aot_inductor::ConstantType::Parameter);
	    constants_info_[1].shape = {16};
	    constants_info_[1].stride = {1};
	    constants_info_[1].layout = static_cast<int32_t>(cached_torch_layout_strided);
	    constants_info_[1].original_fqn = "fc1.bias";
	    update_constants_map(std::move(constants_map));
	    update_constants_array(std::move(constants_array));
	    in_spec_ = "[1, {\"type\": \"builtins.tuple\", \"context\": \"null\", \"children_spec\": [{\"type\": \"builtins.tuple\", \"context\": \"null\", \"children_spec\": [{\"type\": null, \"context\": null, \"children_spec\": []}, {\"type\": null, \"context\": null, \"children_spec\": []}]}, {\"type\": \"builtins.dict\", \"context\": \"[]\", \"children_spec\": []}]}]";
	    out_spec_ = "[1, {\"type\": null, \"context\": null, \"children_spec\": []}]";
	    outputs_info_[0].name = "output0";
	    this->kernels_ = std::make_unique<AOTInductorModelKernels>();
	}
	
	std::unordered_map<std::string, AtenTensorHandle> AOTInductorModel::const_run_impl(
	    DeviceStreamType stream,
	    AOTIProxyExecutorHandle proxy_executor,
	    bool initialization
	) {
	
	    if (!initialization) {
	        std::cerr << "[WARNING] Calling constant_folding in model, but compiled with config: "
	                  << "aot_inductor.use_runtime_constant_folding=False\n";
	    }
	    return {};
	}
	
	void AOTInductorModel::_const_run_impl(
	    std::vector<AtenTensorHandle>& output_handles,
	    DeviceStreamType stream,
	    AOTIProxyExecutorHandle proxy_executor
	) {}
	
	void AOTInductorModel::run_impl(
	    AtenTensorHandle*
	        input_handles, // array of input AtenTensorHandle; handles
	                        // are stolen; the array itself is borrowed
	    AtenTensorHandle*
	        output_handles, // array for writing output AtenTensorHandle; handles
	                        // will be stolen by the caller; the array itself is
	                        // borrowed
	    DeviceStreamType stream,
	    AOTIProxyExecutorHandle proxy_executor
	) {
	
	    auto inputs = steal_from_raw_handles_to_raii_handles(input_handles, 2);
	    auto arg2_1 = std::move(inputs[0]);
	    auto arg3_1 = std::move(inputs[1]);
	    [[maybe_unused]] auto fc1_weight = constants_->at(0);
	    [[maybe_unused]] auto fc1_bias = constants_->at(1);
	    inputs.clear();
	    auto& kernels = static_cast<AOTInductorModelKernels&>(*this->kernels_.get());
	
	    AOTICudaStreamGuard stream_guard(stream, this->device_idx_);
	    static constexpr int64_t int_array_2[] = {8L, 16L};
	    static constexpr int64_t int_array_3[] = {16L, 1L};
	    AtenTensorHandle buf0_handle;
	    AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_empty_strided(2, int_array_2, int_array_3, cached_torch_dtype_float32, cached_torch_device_type_cuda, this->device_idx_, &buf0_handle));
	    RAIIAtenTensorHandle buf0(buf0_handle);
	    // Topologically Sorted Source Nodes: [], Original ATen: [aten.addmm]
	    static constexpr int64_t int_array_0[] = {10L, 16L};
	    static constexpr int64_t int_array_1[] = {1L, 10L};
	    auto tmp_tensor_handle_0 = reinterpret_tensor_wrapper(fc1_weight, 2, int_array_0, int_array_1, 0L);
	    RAIIAtenTensorHandle tmp_tensor_handle_0_raii(tmp_tensor_handle_0);
	    AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_cuda_mm_out(buf0, arg2_1, tmp_tensor_handle_0_raii));
	    arg2_1.reset();
	    auto buf1 = std::move(buf0);  // reuse
	    // Topologically Sorted Source Nodes: [add, relu, sigmoid], Original ATen: [aten.addmm, aten.relu, aten.sigmoid]
	    if (kernels.triton_poi_fused_addmm_relu_sigmoid_0 == nullptr) {
	        kernels.triton_poi_fused_addmm_relu_sigmoid_0 = loadKernel("/tmp/torchinductor_shangdiy/clfxsfxv4tjfonuwp2w6qbwpgbdh7xotkmm5r6jfm6i6nvldgzlp/cgtwzymffpxo3wova6pv7gfh3t4tcgubjsugq6uw6vksmob4mmh4.cubin", "triton_poi_fused_addmm_relu_sigmoid_0", 0, this->cubin_dir_);
	    }
	    CUdeviceptr var_0 = reinterpret_cast<CUdeviceptr>(buf1.data_ptr());
	    CUdeviceptr var_1 = reinterpret_cast<CUdeviceptr>(fc1_bias.data_ptr());
	    int var_2 = 128L;
	    void* kernel_args_var_0[] = {&var_0, &var_1, &var_2};
	    Grid triton_poi_fused_addmm_relu_sigmoid_0_grid_0 = Grid(1L, 1L, 1L);
	    if (triton_poi_fused_addmm_relu_sigmoid_0_grid_0.is_non_zero()) {
	        launchKernel(kernels.triton_poi_fused_addmm_relu_sigmoid_0, triton_poi_fused_addmm_relu_sigmoid_0_grid_0.grid_x, triton_poi_fused_addmm_relu_sigmoid_0_grid_0.grid_y, triton_poi_fused_addmm_relu_sigmoid_0_grid_0.grid_z, 4, 0, kernel_args_var_0, stream);
	    }
	    output_handles[0] = buf1.release();
	} // AOTInductorModel::run_impl
	} // namespace torch::aot_inductor
	
	
	
	
V0213 10:07:37.374000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "96dfc667b274e18ec890d51917192023"}
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V0213 10:07:42.751000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "5c20f71e44e14eaf053cf21e3d543355"}
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V0213 10:07:42.756000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "15bc205dcc80c1c003a45f70511e3f90"}
	{
	"name": "run_command_and_check",
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V0213 10:07:42.786000 132701 /data/users/shangdiy/fbsource/fbcode/caffe2/torch/_dynamo/utils.py:1762] {"chromium_event": {}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "7a53447f8a78a931dd19e9c9f87d8ea5"}
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